Parkinsons_Speach All Options

1 Parkinsons Univariate Options Analysis

1.0.1 Loading the libraries

library("FRESA.CAD")
library(readxl)
library("whitening")
library("fpc")
library("robust")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
TrainFraction <- 0.50;

dataLoad = FALSE

1.1 The Data

pd_speech_features <- as.data.frame(read_excel("~/GitHub/FCA/Data/pd_speech_features.xlsx",sheet = "pd_speech_features", range = "A2:ACB758"))

1.1.1 The Average of the Three Repetitions

Each subject had three repeated observations. Here I’ll use the average of the three experiments per subject.

rep1Parkison <- subset(pd_speech_features,RID==1)
rownames(rep1Parkison) <- rep1Parkison$id
rep1Parkison$id <- NULL
rep1Parkison$RID <- NULL
rep1Parkison[,1:ncol(rep1Parkison)] <- sapply(rep1Parkison,as.numeric)

rep2Parkison <- subset(pd_speech_features,RID==2)
rownames(rep2Parkison) <- rep2Parkison$id
rep2Parkison$id <- NULL
rep2Parkison$RID <- NULL
rep2Parkison[,1:ncol(rep2Parkison)] <- sapply(rep2Parkison,as.numeric)

rep3Parkison <- subset(pd_speech_features,RID==3)
rownames(rep3Parkison) <- rep3Parkison$id
rep3Parkison$id <- NULL
rep3Parkison$RID <- NULL
rep3Parkison[,1:ncol(rep3Parkison)] <- sapply(rep3Parkison,as.numeric)

whof <- !(colnames(rep1Parkison) %in% c("gender","class"));
avgParkison <- rep1Parkison;
avgParkison[,whof] <- (rep1Parkison[,whof] + rep2Parkison[,whof] + rep3Parkison[,whof])/3

pander::pander(table(avgParkison$class))

0	1
64	188

set.seed(1)
trainCases <- sample(nrow(avgParkison),nrow(avgParkison)*TrainFraction)

avgParkison_train <- avgParkison[trainCases,]
avgParkison_test <- avgParkison[-trainCases,]

pander::pander(table(avgParkison_train$class))

0	1
31	95

pander::pander(table(avgParkison_test$class))

0	1
33	93

1.1.1.1 Analysis parameters

dataframe <- avgParkison_train
dataframeTest <- avgParkison_test
outcome <- "class"

1.1.2 All the run options

sig_pvalue <- 0.05
thr <- c(0.05,0.2,0.4,0.6,0.8,0.95);
method  <- c("fast","pearson","spearman");
type <- c("LM","RLM")
DeOutcome <- c("T_Blind","T_Driven")
corRank <- c(FALSE,TRUE)

#method  <- c("fast","pearson","spearman");
#type <- c("LM","RLM")

1.2 Decorrelation Analysis

if (dataLoad)
{
  load("~/GitHub/LatentBiomarkers/RMD/Parkinsons_ALL_Options.RData")
} else
{
  
  idx = 0;
  thenames <- list();
  totBaM <- NULL
  totDeM <- NULL
  toUnmatM <- NULL
  unalteredM <- NULL
  Decorrleated_FractionM<- NULL
  Base_FractionM<- NULL
  Unaltered_FractionM <- NULL
  sparcityM <- NULL
  Average_Latent_SizeM <- NULL
  SigDeM <- NULL
  La_SignificantM <- NULL
  pbKNNaucM <- NULL
  pbKNNaccM <- NULL
  
  for (DeOut in DeOutcome)
  {
    for (meth in method)
    {
      for (typ in type)
      {
        for (corran in corRank)
        {
          par(op)
          par(mfrow=c(3,2),cex=0.5)
          idx <- idx + 1;
          thenames[[idx]] <- paste(DeOut,meth,typ,corran,sep="_")
          totBa <- numeric()
          totDe <- numeric()
          toUnmat <- numeric()
          unaltered <- numeric()
          Decorrleated_Fraction<- numeric()
          Base_Fraction<- numeric()
          Unaltered_Fraction <- numeric()
          sparcity <- numeric()
          Average_Latent_Size  <- numeric()
          SigDe <- numeric()
          La_Significant <- numeric()
          pbKNNauc <- numeric()
          pbKNNacc <- numeric()
          for (thrs in thr)
          {
            print(thenames[[idx]])
            plotname <- paste(thenames[[idx]],thrs,sep="_")
            
  
            if (DeOut != "T_Driven")
            {
              DEdataframeTrain <- IDeA(dataframe,
                                                     thr=thrs,
                                                     method=meth,
                                                     type=typ,
                                                     corRank=corran,
                                                     relaxed=TRUE,
                                                     verbose = FALSE)
            } else
            {
              DEdataframeTrain <- IDeA(dataframe,
                                                     Outcome=outcome,
                                                     thr=thrs,
                                                     method=meth,
                                                     type=typ,
                                                     corRank=corran,
                                                     relaxed=TRUE,
                                                     verbose = FALSE)
            }
            demat <- attr(DEdataframeTrain,"UPSTM")
            DEdataframe <- predictDecorrelate(DEdataframeTrain,dataframeTest)
            
            totFe <- ncol(DEdataframe)-1
            totBa <- c(totBa,length(attr(DEdataframeTrain,"unaltered")))
            totDe <- c(totDe, sum(str_detect(colnames(DEdataframe),"La_")))
            toUn <- sum(apply(demat!=0,2,sum)==1)
            toUnmat <- c(toUnmat,toUn )
            una <-  totFe - ncol(demat) + toUn
            unaltered <- c(unaltered,una)
            Decorrleated_Fraction <- c(Decorrleated_Fraction,sum(str_detect(colnames(DEdataframe),"La_"))/totFe)
            Base_Fraction <- c(Base_Fraction,length(attr(DEdataframeTrain,"unaltered"))/totFe)
#            print(totBa)
            
            Unaltered_Fraction <- c(Unaltered_Fraction,una/totFe)
            sparcity <- c(sparcity,(totFe-ncol(demat)+sum(abs(demat)!=0))/totFe/totFe)
            
            varlistDe <-  colnames(demat)[apply(demat!=0,2,sum)>1];
            varlistDe <- as.data.frame(cbind(name=varlistDe,desc=varlistDe))
            
            
            varlist_DeAll <- colnames(DEdataframe)
            varlist_DeAll <- varlist_DeAll[!(varlist_DeAll %in% c(outcome))]
            varlist_DeAll <- as.data.frame(cbind(name=varlist_DeAll,desc=varlist_DeAll))
            
  
            
            pDe <- univariate_Wilcoxon(DEdataframeTrain,outcome,pvalue=sig_pvalue);
            topDecorNames <- names(pDe);
  
            dc <- getLatentCoefficients(DEdataframeTrain)
            deNames_in_dc <- topDecorNames[topDecorNames %in% names(dc)]
            selectedlist <- dc[deNames_in_dc]
            theDeFormulas <- selectedlist
            
            
            Average_Latent_Size <- c(Average_Latent_Size,length(unlist(theDeFormulas))/length(theDeFormulas))
            
            topSigDe <- topDecorNames
            
            pDeDe <- pDe[names(pDe) %in% varlistDe[,1]]

            La_Significant <- c(La_Significant,length(topSigDe))
            SigDe <- c(SigDe,length(pDeDe))
  
            
            mlKNN <- KNN_method(formula(paste(outcome,"~.")),DEdataframeTrain[,c(outcome,topSigDe)])
            psb <- predictionStats_binary(cbind(dataframeTest[,outcome],
                                                  predict(mlKNN,
                                                  DEdataframe[,c(outcome,topSigDe)])),plotname,cex=0.6)
            pbKNNauc <- c(pbKNNauc,psb$aucs[1])
            pbKNNacc <- c(pbKNNacc,psb$accc[1])
          }
          totBaM <- rbind(totBaM,totBa)
          totDeM <- rbind(totDeM,totDe)
          toUnmatM <- rbind(toUnmatM,toUnmat)
          unalteredM <- rbind(unalteredM,unaltered)
          Decorrleated_FractionM <- rbind(Decorrleated_FractionM,Decorrleated_Fraction)
          Base_FractionM <- rbind(Base_FractionM,Base_Fraction)
          Unaltered_FractionM <- rbind(Unaltered_FractionM,Unaltered_Fraction)
          sparcityM <- rbind(sparcityM,sparcity)
          Average_Latent_SizeM <- rbind(Average_Latent_SizeM,Average_Latent_Size)
          SigDeM <- rbind(SigDeM,SigDe)
          La_SignificantM <- rbind(La_SignificantM,La_Significant)
          pbKNNaucM <- rbind(pbKNNaucM,pbKNNauc)
          pbKNNaccM <- rbind(pbKNNaccM,pbKNNacc)
  
        }
      }
    }
  }
}

[1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.05 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.2 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.4 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.6 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.8 [1] “T_Blind_fast_LM_FALSE” T_Blind_fast_LM_FALSE_0.95 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.05 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.2 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.4 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.6 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.8 [1] “T_Blind_fast_LM_TRUE” T_Blind_fast_LM_TRUE_0.95 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.05 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.2 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.4 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.6 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.8 [1] “T_Blind_fast_RLM_FALSE” T_Blind_fast_RLM_FALSE_0.95 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.05 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.2 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.4 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.6 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.8 [1] “T_Blind_fast_RLM_TRUE” T_Blind_fast_RLM_TRUE_0.95 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.05 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.2 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.4 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.6 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.8 [1] “T_Blind_pearson_LM_FALSE” T_Blind_pearson_LM_FALSE_0.95 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.05 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.2 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.4 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.6 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.8 [1] “T_Blind_pearson_LM_TRUE” T_Blind_pearson_LM_TRUE_0.95 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.05 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.2 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.4 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.6 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.8 [1] “T_Blind_pearson_RLM_FALSE” T_Blind_pearson_RLM_FALSE_0.95 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.05 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.2 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.4 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.6 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.8 [1] “T_Blind_pearson_RLM_TRUE” T_Blind_pearson_RLM_TRUE_0.95 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.05 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.2 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.4 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.6 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.8 [1] “T_Blind_spearman_LM_FALSE” T_Blind_spearman_LM_FALSE_0.95 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.05 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.2 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.4 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.6 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.8 [1] “T_Blind_spearman_LM_TRUE” T_Blind_spearman_LM_TRUE_0.95 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.05 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.2 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.4 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.6 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.8 [1] “T_Blind_spearman_RLM_FALSE” T_Blind_spearman_RLM_FALSE_0.95 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.05 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.2 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.4 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.6 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.8 [1] “T_Blind_spearman_RLM_TRUE” T_Blind_spearman_RLM_TRUE_0.95 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.05 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.2 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.4 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.6 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.8 [1] “T_Driven_fast_LM_FALSE” T_Driven_fast_LM_FALSE_0.95 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.05 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.2 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.4 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.6 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.8 [1] “T_Driven_fast_LM_TRUE” T_Driven_fast_LM_TRUE_0.95 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.05 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.2 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.4 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.6 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.8 [1] “T_Driven_fast_RLM_FALSE” T_Driven_fast_RLM_FALSE_0.95 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.05 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.2 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.4 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.6 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.8 [1] “T_Driven_fast_RLM_TRUE” T_Driven_fast_RLM_TRUE_0.95 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.05 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.2 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.4 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.6 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.8 [1] “T_Driven_pearson_LM_FALSE” T_Driven_pearson_LM_FALSE_0.95 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.05 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.2 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.4 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.6 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.8 [1] “T_Driven_pearson_LM_TRUE” T_Driven_pearson_LM_TRUE_0.95 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.05 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.2 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.4 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.6 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.8 [1] “T_Driven_pearson_RLM_FALSE” T_Driven_pearson_RLM_FALSE_0.95 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.05 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.2 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.4 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.6 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.8 [1] “T_Driven_pearson_RLM_TRUE” T_Driven_pearson_RLM_TRUE_0.95 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.05 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.2 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.4 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.6 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.8 [1] “T_Driven_spearman_LM_FALSE” T_Driven_spearman_LM_FALSE_0.95 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.05 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.2 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.4 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.6 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.8 [1] “T_Driven_spearman_LM_TRUE” T_Driven_spearman_LM_TRUE_0.95 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.05 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.2 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.4 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.6 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.8 [1] “T_Driven_spearman_RLM_FALSE” T_Driven_spearman_RLM_FALSE_0.95 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.05 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.2 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.4 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.6 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.8 [1] “T_Driven_spearman_RLM_TRUE” T_Driven_spearman_RLM_TRUE_0.95

par(op)

1.2.1 Printing the analysis outputs

par(op)
par(mfrow=c(1,2),cex=0.6)

rownames(totBaM) <- thenames
rownames(totDeM) <- thenames
rownames(toUnmatM) <- thenames
rownames(unalteredM) <- thenames
rownames(Decorrleated_FractionM) <- thenames
rownames(Base_FractionM) <- thenames
rownames(Unaltered_FractionM) <- thenames
rownames(sparcityM) <- thenames
rownames(Average_Latent_SizeM) <- thenames
rownames(SigDeM) <- thenames
rownames(La_SignificantM) <- thenames
rownames(pbKNNaucM) <- thenames
rownames(pbKNNaccM) <- thenames

colnames(totBaM) <- thr
colnames(totDeM) <- thr
colnames(toUnmatM) <- thr
colnames(unalteredM) <- thr
colnames(Decorrleated_FractionM) <- thr
colnames(Base_FractionM) <- thr
colnames(Unaltered_FractionM) <- thr
colnames(sparcityM) <- thr
colnames(Average_Latent_SizeM) <- thr
colnames(SigDeM) <- thr
colnames(La_SignificantM) <- thr
colnames(pbKNNaucM) <- thr
colnames(pbKNNaccM) <- thr

pander::pander(totFe)

753

pander::pander(totBaM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	1	3	36	55	74	114
T_Blind_fast_LM_TRUE	6	1	22	39	49	104
T_Blind_fast_RLM_FALSE	1	3	36	55	74	114
T_Blind_fast_RLM_TRUE	6	1	22	39	49	104
T_Blind_pearson_LM_FALSE	1	3	35	55	74	114
T_Blind_pearson_LM_TRUE	1	5	22	39	49	104
T_Blind_pearson_RLM_FALSE	29	20	41	55	69	114
T_Blind_pearson_RLM_TRUE	39	34	42	36	48	104
T_Blind_spearman_LM_FALSE	9	8	30	40	66	103
T_Blind_spearman_LM_TRUE	9	5	29	31	49	86
T_Blind_spearman_RLM_FALSE	17	13	24	40	65	99
T_Blind_spearman_RLM_TRUE	16	10	18	28	46	84
T_Driven_fast_LM_FALSE	19	20	34	57	71	116
T_Driven_fast_LM_TRUE	11	13	30	44	52	106
T_Driven_fast_RLM_FALSE	19	20	34	57	71	116
T_Driven_fast_RLM_TRUE	11	13	30	44	52	106
T_Driven_pearson_LM_FALSE	18	18	34	57	71	116
T_Driven_pearson_LM_TRUE	8	9	31	44	52	106
T_Driven_pearson_RLM_FALSE	30	35	52	64	71	118
T_Driven_pearson_RLM_TRUE	29	41	47	53	57	108
T_Driven_spearman_LM_FALSE	29	22	35	41	66	106
T_Driven_spearman_LM_TRUE	21	18	31	39	54	92
T_Driven_spearman_RLM_FALSE	26	28	36	45	65	102
T_Driven_spearman_RLM_TRUE	17	21	33	37	48	89

pander::pander(totDeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	751	749	648	571	483	306
T_Blind_fast_LM_TRUE	737	751	665	590	507	315
T_Blind_fast_RLM_FALSE	751	749	648	571	483	306
T_Blind_fast_RLM_TRUE	737	751	665	590	507	315
T_Blind_pearson_LM_FALSE	751	749	648	571	483	306
T_Blind_pearson_LM_TRUE	751	712	665	590	507	315
T_Blind_pearson_RLM_FALSE	596	623	596	553	477	304
T_Blind_pearson_RLM_TRUE	574	589	573	564	498	310
T_Blind_spearman_LM_FALSE	638	625	596	564	483	336
T_Blind_spearman_LM_TRUE	628	650	593	574	500	356
T_Blind_spearman_RLM_FALSE	599	602	610	569	485	335
T_Blind_spearman_RLM_TRUE	614	615	618	581	500	355
T_Driven_fast_LM_FALSE	699	695	649	569	486	303
T_Driven_fast_LM_TRUE	684	692	658	585	506	313
T_Driven_fast_RLM_FALSE	699	695	649	569	486	303
T_Driven_fast_RLM_TRUE	684	692	658	585	506	313
T_Driven_pearson_LM_FALSE	695	705	649	569	486	303
T_Driven_pearson_LM_TRUE	697	711	655	585	506	313
T_Driven_pearson_RLM_FALSE	606	594	570	535	471	297
T_Driven_pearson_RLM_TRUE	601	579	569	546	486	303
T_Driven_spearman_LM_FALSE	586	602	590	565	486	333
T_Driven_spearman_LM_TRUE	600	613	601	575	495	352
T_Driven_spearman_RLM_FALSE	583	581	592	566	486	334
T_Driven_spearman_RLM_TRUE	606	609	595	571	498	351

pander::pander(toUnmatM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	1	3	36	55	74	114
T_Blind_fast_LM_TRUE	6	1	22	39	49	104
T_Blind_fast_RLM_FALSE	1	3	36	55	74	114
T_Blind_fast_RLM_TRUE	6	1	22	39	49	104
T_Blind_pearson_LM_FALSE	1	3	35	55	74	114
T_Blind_pearson_LM_TRUE	1	5	22	39	49	104
T_Blind_pearson_RLM_FALSE	29	20	41	55	69	114
T_Blind_pearson_RLM_TRUE	39	34	42	36	48	104
T_Blind_spearman_LM_FALSE	9	8	30	40	66	103
T_Blind_spearman_LM_TRUE	9	5	29	31	49	86
T_Blind_spearman_RLM_FALSE	17	13	24	40	65	99
T_Blind_spearman_RLM_TRUE	16	10	18	28	46	84
T_Driven_fast_LM_FALSE	19	20	34	57	71	116
T_Driven_fast_LM_TRUE	11	13	30	44	52	106
T_Driven_fast_RLM_FALSE	19	20	34	57	71	116
T_Driven_fast_RLM_TRUE	11	13	30	44	52	106
T_Driven_pearson_LM_FALSE	18	18	34	57	71	116
T_Driven_pearson_LM_TRUE	8	9	31	44	52	106
T_Driven_pearson_RLM_FALSE	30	35	52	64	71	118
T_Driven_pearson_RLM_TRUE	29	41	47	53	57	108
T_Driven_spearman_LM_FALSE	29	22	35	41	66	106
T_Driven_spearman_LM_TRUE	21	18	31	39	54	92
T_Driven_spearman_RLM_FALSE	26	28	36	45	65	102
T_Driven_spearman_RLM_TRUE	17	21	33	37	48	89

pander::pander(unalteredM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	2	4	105	182	270	447
T_Blind_fast_LM_TRUE	16	2	88	163	246	438
T_Blind_fast_RLM_FALSE	2	4	105	182	270	447
T_Blind_fast_RLM_TRUE	16	2	88	163	246	438
T_Blind_pearson_LM_FALSE	2	4	105	182	270	447
T_Blind_pearson_LM_TRUE	2	41	88	163	246	438
T_Blind_pearson_RLM_FALSE	157	130	157	200	276	449
T_Blind_pearson_RLM_TRUE	179	164	180	189	255	443
T_Blind_spearman_LM_FALSE	115	128	157	189	270	417
T_Blind_spearman_LM_TRUE	125	103	160	179	253	397
T_Blind_spearman_RLM_FALSE	154	151	143	184	268	418
T_Blind_spearman_RLM_TRUE	139	138	135	172	253	398
T_Driven_fast_LM_FALSE	54	58	104	184	267	450
T_Driven_fast_LM_TRUE	69	61	95	168	247	440
T_Driven_fast_RLM_FALSE	54	58	104	184	267	450
T_Driven_fast_RLM_TRUE	69	61	95	168	247	440
T_Driven_pearson_LM_FALSE	58	48	104	184	267	450
T_Driven_pearson_LM_TRUE	56	42	98	168	247	440
T_Driven_pearson_RLM_FALSE	147	159	183	218	282	456
T_Driven_pearson_RLM_TRUE	152	174	184	207	267	450
T_Driven_spearman_LM_FALSE	167	151	163	188	267	420
T_Driven_spearman_LM_TRUE	153	140	152	178	258	401
T_Driven_spearman_RLM_FALSE	170	172	161	187	267	419
T_Driven_spearman_RLM_TRUE	147	144	158	182	255	402

pander::pander(Decorrleated_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.997	0.995	0.861	0.758	0.641	0.406
T_Blind_fast_LM_TRUE	0.979	0.997	0.883	0.784	0.673	0.418
T_Blind_fast_RLM_FALSE	0.997	0.995	0.861	0.758	0.641	0.406
T_Blind_fast_RLM_TRUE	0.979	0.997	0.883	0.784	0.673	0.418
T_Blind_pearson_LM_FALSE	0.997	0.995	0.861	0.758	0.641	0.406
T_Blind_pearson_LM_TRUE	0.997	0.946	0.883	0.784	0.673	0.418
T_Blind_pearson_RLM_FALSE	0.792	0.827	0.792	0.734	0.633	0.404
T_Blind_pearson_RLM_TRUE	0.762	0.782	0.761	0.749	0.661	0.412
T_Blind_spearman_LM_FALSE	0.847	0.830	0.792	0.749	0.641	0.446
T_Blind_spearman_LM_TRUE	0.834	0.863	0.788	0.762	0.664	0.473
T_Blind_spearman_RLM_FALSE	0.795	0.799	0.810	0.756	0.644	0.445
T_Blind_spearman_RLM_TRUE	0.815	0.817	0.821	0.772	0.664	0.471
T_Driven_fast_LM_FALSE	0.928	0.923	0.862	0.756	0.645	0.402
T_Driven_fast_LM_TRUE	0.908	0.919	0.874	0.777	0.672	0.416
T_Driven_fast_RLM_FALSE	0.928	0.923	0.862	0.756	0.645	0.402
T_Driven_fast_RLM_TRUE	0.908	0.919	0.874	0.777	0.672	0.416
T_Driven_pearson_LM_FALSE	0.923	0.936	0.862	0.756	0.645	0.402
T_Driven_pearson_LM_TRUE	0.926	0.944	0.870	0.777	0.672	0.416
T_Driven_pearson_RLM_FALSE	0.805	0.789	0.757	0.710	0.625	0.394
T_Driven_pearson_RLM_TRUE	0.798	0.769	0.756	0.725	0.645	0.402
T_Driven_spearman_LM_FALSE	0.778	0.799	0.784	0.750	0.645	0.442
T_Driven_spearman_LM_TRUE	0.797	0.814	0.798	0.764	0.657	0.467
T_Driven_spearman_RLM_FALSE	0.774	0.772	0.786	0.752	0.645	0.444
T_Driven_spearman_RLM_TRUE	0.805	0.809	0.790	0.758	0.661	0.466

pander::pander(Base_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.00133	0.00398	0.0478	0.0730	0.0983	0.151
T_Blind_fast_LM_TRUE	0.00797	0.00133	0.0292	0.0518	0.0651	0.138
T_Blind_fast_RLM_FALSE	0.00133	0.00398	0.0478	0.0730	0.0983	0.151
T_Blind_fast_RLM_TRUE	0.00797	0.00133	0.0292	0.0518	0.0651	0.138
T_Blind_pearson_LM_FALSE	0.00133	0.00398	0.0465	0.0730	0.0983	0.151
T_Blind_pearson_LM_TRUE	0.00133	0.00664	0.0292	0.0518	0.0651	0.138
T_Blind_pearson_RLM_FALSE	0.03851	0.02656	0.0544	0.0730	0.0916	0.151
T_Blind_pearson_RLM_TRUE	0.05179	0.04515	0.0558	0.0478	0.0637	0.138
T_Blind_spearman_LM_FALSE	0.01195	0.01062	0.0398	0.0531	0.0876	0.137
T_Blind_spearman_LM_TRUE	0.01195	0.00664	0.0385	0.0412	0.0651	0.114
T_Blind_spearman_RLM_FALSE	0.02258	0.01726	0.0319	0.0531	0.0863	0.131
T_Blind_spearman_RLM_TRUE	0.02125	0.01328	0.0239	0.0372	0.0611	0.112
T_Driven_fast_LM_FALSE	0.02523	0.02656	0.0452	0.0757	0.0943	0.154
T_Driven_fast_LM_TRUE	0.01461	0.01726	0.0398	0.0584	0.0691	0.141
T_Driven_fast_RLM_FALSE	0.02523	0.02656	0.0452	0.0757	0.0943	0.154
T_Driven_fast_RLM_TRUE	0.01461	0.01726	0.0398	0.0584	0.0691	0.141
T_Driven_pearson_LM_FALSE	0.02390	0.02390	0.0452	0.0757	0.0943	0.154
T_Driven_pearson_LM_TRUE	0.01062	0.01195	0.0412	0.0584	0.0691	0.141
T_Driven_pearson_RLM_FALSE	0.03984	0.04648	0.0691	0.0850	0.0943	0.157
T_Driven_pearson_RLM_TRUE	0.03851	0.05445	0.0624	0.0704	0.0757	0.143
T_Driven_spearman_LM_FALSE	0.03851	0.02922	0.0465	0.0544	0.0876	0.141
T_Driven_spearman_LM_TRUE	0.02789	0.02390	0.0412	0.0518	0.0717	0.122
T_Driven_spearman_RLM_FALSE	0.03453	0.03718	0.0478	0.0598	0.0863	0.135
T_Driven_spearman_RLM_TRUE	0.02258	0.02789	0.0438	0.0491	0.0637	0.118

pander::pander(Unaltered_FractionM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.00266	0.00531	0.139	0.242	0.359	0.594
T_Blind_fast_LM_TRUE	0.02125	0.00266	0.117	0.216	0.327	0.582
T_Blind_fast_RLM_FALSE	0.00266	0.00531	0.139	0.242	0.359	0.594
T_Blind_fast_RLM_TRUE	0.02125	0.00266	0.117	0.216	0.327	0.582
T_Blind_pearson_LM_FALSE	0.00266	0.00531	0.139	0.242	0.359	0.594
T_Blind_pearson_LM_TRUE	0.00266	0.05445	0.117	0.216	0.327	0.582
T_Blind_pearson_RLM_FALSE	0.20850	0.17264	0.208	0.266	0.367	0.596
T_Blind_pearson_RLM_TRUE	0.23772	0.21780	0.239	0.251	0.339	0.588
T_Blind_spearman_LM_FALSE	0.15272	0.16999	0.208	0.251	0.359	0.554
T_Blind_spearman_LM_TRUE	0.16600	0.13679	0.212	0.238	0.336	0.527
T_Blind_spearman_RLM_FALSE	0.20452	0.20053	0.190	0.244	0.356	0.555
T_Blind_spearman_RLM_TRUE	0.18459	0.18327	0.179	0.228	0.336	0.529
T_Driven_fast_LM_FALSE	0.07171	0.07703	0.138	0.244	0.355	0.598
T_Driven_fast_LM_TRUE	0.09163	0.08101	0.126	0.223	0.328	0.584
T_Driven_fast_RLM_FALSE	0.07171	0.07703	0.138	0.244	0.355	0.598
T_Driven_fast_RLM_TRUE	0.09163	0.08101	0.126	0.223	0.328	0.584
T_Driven_pearson_LM_FALSE	0.07703	0.06375	0.138	0.244	0.355	0.598
T_Driven_pearson_LM_TRUE	0.07437	0.05578	0.130	0.223	0.328	0.584
T_Driven_pearson_RLM_FALSE	0.19522	0.21116	0.243	0.290	0.375	0.606
T_Driven_pearson_RLM_TRUE	0.20186	0.23108	0.244	0.275	0.355	0.598
T_Driven_spearman_LM_FALSE	0.22178	0.20053	0.216	0.250	0.355	0.558
T_Driven_spearman_LM_TRUE	0.20319	0.18592	0.202	0.236	0.343	0.533
T_Driven_spearman_RLM_FALSE	0.22576	0.22842	0.214	0.248	0.355	0.556
T_Driven_spearman_RLM_TRUE	0.19522	0.19124	0.210	0.242	0.339	0.534

pander::pander(sparcityM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.85486	0.83684	0.02432	0.00418	0.00254	0.00199
T_Blind_fast_LM_TRUE	0.14325	0.95355	0.03543	0.00480	0.00279	0.00206
T_Blind_fast_RLM_FALSE	0.85486	0.83684	0.02432	0.00418	0.00254	0.00199
T_Blind_fast_RLM_TRUE	0.14325	0.95355	0.03543	0.00480	0.00279	0.00206
T_Blind_pearson_LM_FALSE	0.85022	0.86646	0.02609	0.00418	0.00254	0.00199
T_Blind_pearson_LM_TRUE	0.72133	0.05020	0.03543	0.00480	0.00279	0.00206
T_Blind_pearson_RLM_FALSE	0.00395	0.00452	0.00387	0.00331	0.00272	0.00213
T_Blind_pearson_RLM_TRUE	0.00357	0.00425	0.00507	0.00352	0.00317	0.00214
T_Blind_spearman_LM_FALSE	0.00640	0.00609	0.00505	0.00377	0.00272	0.00209
T_Blind_spearman_LM_TRUE	0.00563	0.01403	0.00540	0.00458	0.00291	0.00220
T_Blind_spearman_RLM_FALSE	0.00435	0.00469	0.00590	0.00365	0.00257	0.00204
T_Blind_spearman_RLM_TRUE	0.00429	0.00508	0.00747	0.00396	0.00268	0.00214
T_Driven_fast_LM_FALSE	0.03861	0.03560	0.02301	0.00428	0.00256	0.00198
T_Driven_fast_LM_TRUE	0.01492	0.02229	0.03427	0.00481	0.00277	0.00204
T_Driven_fast_RLM_FALSE	0.03861	0.03560	0.02301	0.00428	0.00256	0.00198
T_Driven_fast_RLM_TRUE	0.01492	0.02229	0.03427	0.00481	0.00277	0.00204
T_Driven_pearson_LM_FALSE	0.03346	0.04483	0.02314	0.00428	0.00256	0.00198
T_Driven_pearson_LM_TRUE	0.01596	0.03711	0.03055	0.00481	0.00277	0.00204
T_Driven_pearson_RLM_FALSE	0.00404	0.00390	0.00349	0.00305	0.00262	0.00204
T_Driven_pearson_RLM_TRUE	0.00366	0.00349	0.00335	0.00318	0.00282	0.00206
T_Driven_spearman_LM_FALSE	0.00465	0.00505	0.00540	0.00396	0.00281	0.00211
T_Driven_spearman_LM_TRUE	0.00468	0.00541	0.00544	0.00472	0.00290	0.00216
T_Driven_spearman_RLM_FALSE	0.00417	0.00410	0.00516	0.00387	0.00255	0.00205
T_Driven_spearman_RLM_TRUE	0.00461	0.00533	0.00526	0.00370	0.00269	0.00211

pander::pander(Average_Latent_SizeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	664.00	627.00	2.50	2.00	2.29	2.00
T_Blind_fast_LM_TRUE	8.00	726.00	8.00	2.00	2.08	2.00
T_Blind_fast_RLM_FALSE	664.00	627.00	2.50	2.00	2.29	2.00
T_Blind_fast_RLM_TRUE	8.00	726.00	8.00	2.00	2.08	2.00
T_Blind_pearson_LM_FALSE	602.50	650.00	3.67	2.00	2.29	2.00
T_Blind_pearson_LM_TRUE	582.00	37.17	8.00	2.00	2.08	2.00
T_Blind_pearson_RLM_FALSE	2.67	3.00	3.60	2.90	2.33	2.11
T_Blind_pearson_RLM_TRUE	2.20	2.17	4.19	2.20	3.09	2.27
T_Blind_spearman_LM_FALSE	4.00	2.50	5.00	3.00	2.23	2.00
T_Blind_spearman_LM_TRUE	2.00	3.33	2.40	3.15	2.00	2.12
T_Blind_spearman_RLM_FALSE	3.40	3.00	8.00	2.25	2.10	2.00
T_Blind_spearman_RLM_TRUE	3.43	2.00	6.80	3.42	2.17	2.08
T_Driven_fast_LM_FALSE	6.00	NA	12.86	3.00	2.12	2.00
T_Driven_fast_LM_TRUE	NA	18.00	NA	2.75	2.36	2.44
T_Driven_fast_RLM_FALSE	6.00	NA	12.86	3.00	2.12	2.00
T_Driven_fast_RLM_TRUE	NA	18.00	NA	2.75	2.36	2.44
T_Driven_pearson_LM_FALSE	NA	NA	12.86	3.00	2.12	2.00
T_Driven_pearson_LM_TRUE	10.00	15.00	5.00	2.75	2.36	2.44
T_Driven_pearson_RLM_FALSE	3.00	3.00	2.33	2.00	2.10	2.12
T_Driven_pearson_RLM_TRUE	2.00	NA	3.00	2.00	2.25	2.58
T_Driven_spearman_LM_FALSE	2.00	5.00	2.00	2.00	2.43	2.25
T_Driven_spearman_LM_TRUE	NA	2.00	2.80	2.83	2.00	2.14
T_Driven_spearman_RLM_FALSE	NA	3.17	3.00	2.33	2.18	2.20
T_Driven_spearman_RLM_TRUE	NA	NA	3.00	3.50	2.62	2.18

pander::pander(SigDeM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	1	1	2	4	7	6
T_Blind_fast_LM_TRUE	1	2	1	2	12	7
T_Blind_fast_RLM_FALSE	1	1	2	4	7	6
T_Blind_fast_RLM_TRUE	1	2	1	2	12	7
T_Blind_pearson_LM_FALSE	2	1	3	4	7	6
T_Blind_pearson_LM_TRUE	1	6	1	2	12	7
T_Blind_pearson_RLM_FALSE	9	8	10	10	15	9
T_Blind_pearson_RLM_TRUE	5	6	16	5	11	11
T_Blind_spearman_LM_FALSE	9	2	3	4	13	3
T_Blind_spearman_LM_TRUE	2	3	5	13	7	8
T_Blind_spearman_RLM_FALSE	5	1	2	4	10	7
T_Blind_spearman_RLM_TRUE	7	1	5	12	6	13
T_Driven_fast_LM_FALSE	1	0	7	8	8	5
T_Driven_fast_LM_TRUE	0	3	0	8	11	9
T_Driven_fast_RLM_FALSE	1	0	7	8	8	5
T_Driven_fast_RLM_TRUE	0	3	0	8	11	9
T_Driven_pearson_LM_FALSE	0	0	7	8	8	5
T_Driven_pearson_LM_TRUE	1	1	1	8	11	9
T_Driven_pearson_RLM_FALSE	3	9	3	2	10	8
T_Driven_pearson_RLM_TRUE	1	0	3	2	8	12
T_Driven_spearman_LM_FALSE	2	1	1	2	7	4
T_Driven_spearman_LM_TRUE	0	3	5	6	9	7
T_Driven_spearman_RLM_FALSE	0	6	4	9	11	10
T_Driven_spearman_RLM_TRUE	0	0	2	6	8	17

pander::pander(La_SignificantM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	1	1	2	6	24	65
T_Blind_fast_LM_TRUE	1	2	1	2	31	64
T_Blind_fast_RLM_FALSE	1	1	2	6	24	65
T_Blind_fast_RLM_TRUE	1	2	1	2	31	64
T_Blind_pearson_LM_FALSE	2	1	3	6	24	65
T_Blind_pearson_LM_TRUE	1	7	1	2	31	64
T_Blind_pearson_RLM_FALSE	10	9	13	17	40	70
T_Blind_pearson_RLM_TRUE	10	10	22	13	29	70
T_Blind_spearman_LM_FALSE	9	2	4	10	36	50
T_Blind_spearman_LM_TRUE	3	4	7	21	24	54
T_Blind_spearman_RLM_FALSE	6	1	2	10	29	56
T_Blind_spearman_RLM_TRUE	8	3	6	17	20	61
T_Driven_fast_LM_FALSE	2	2	9	17	29	65
T_Driven_fast_LM_TRUE	1	4	1	17	33	72
T_Driven_fast_RLM_FALSE	2	2	9	17	29	65
T_Driven_fast_RLM_TRUE	1	4	1	17	33	72
T_Driven_pearson_LM_FALSE	1	2	9	17	29	65
T_Driven_pearson_LM_TRUE	2	2	2	17	33	72
T_Driven_pearson_RLM_FALSE	5	14	8	9	36	70
T_Driven_pearson_RLM_TRUE	2	3	8	9	29	75
T_Driven_spearman_LM_FALSE	3	2	3	8	24	54
T_Driven_spearman_LM_TRUE	1	4	7	14	31	56
T_Driven_spearman_RLM_FALSE	1	8	6	16	33	62
T_Driven_spearman_RLM_TRUE	1	1	4	14	25	69

pander::pander(pbKNNaucM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.408	0.607	0.652	0.758	0.841	0.827
T_Blind_fast_LM_TRUE	0.617	0.503	0.595	0.694	0.838	0.802
T_Blind_fast_RLM_FALSE	0.408	0.607	0.652	0.758	0.841	0.827
T_Blind_fast_RLM_TRUE	0.617	0.503	0.595	0.694	0.838	0.802
T_Blind_pearson_LM_FALSE	0.498	0.504	0.695	0.758	0.841	0.827
T_Blind_pearson_LM_TRUE	0.600	0.720	0.595	0.694	0.838	0.802
T_Blind_pearson_RLM_FALSE	0.727	0.733	0.739	0.780	0.775	0.798
T_Blind_pearson_RLM_TRUE	0.772	0.847	0.785	0.843	0.824	0.782
T_Blind_spearman_LM_FALSE	0.803	0.753	0.785	0.795	0.833	0.814
T_Blind_spearman_LM_TRUE	0.746	0.776	0.775	0.797	0.825	0.831
T_Blind_spearman_RLM_FALSE	0.738	0.504	0.623	0.794	0.805	0.737
T_Blind_spearman_RLM_TRUE	0.671	0.739	0.741	0.770	0.784	0.785
T_Driven_fast_LM_FALSE	0.732	0.740	0.780	0.852	0.836	0.798
T_Driven_fast_LM_TRUE	0.607	0.683	0.721	0.819	0.868	0.797
T_Driven_fast_RLM_FALSE	0.732	0.740	0.780	0.852	0.836	0.798
T_Driven_fast_RLM_TRUE	0.607	0.683	0.721	0.819	0.868	0.797
T_Driven_pearson_LM_FALSE	0.607	0.740	0.780	0.852	0.836	0.798
T_Driven_pearson_LM_TRUE	0.657	0.701	0.720	0.819	0.868	0.797
T_Driven_pearson_RLM_FALSE	0.763	0.717	0.697	0.777	0.754	0.795
T_Driven_pearson_RLM_TRUE	0.763	0.818	0.745	0.818	0.825	0.787
T_Driven_spearman_LM_FALSE	0.759	0.797	0.783	0.838	0.843	0.797
T_Driven_spearman_LM_TRUE	0.607	0.795	0.696	0.789	0.835	0.844
T_Driven_spearman_RLM_FALSE	0.607	0.770	0.636	0.787	0.747	0.706
T_Driven_spearman_RLM_TRUE	0.607	0.799	0.665	0.817	0.809	0.719

pander::pander(pbKNNaccM)

	0.05	0.2	0.4	0.6	0.8	0.95
T_Blind_fast_LM_FALSE	0.651	0.738	0.746	0.817	0.873	0.825
T_Blind_fast_LM_TRUE	0.69	0.579	0.698	0.714	0.873	0.833
T_Blind_fast_RLM_FALSE	0.651	0.738	0.746	0.817	0.873	0.825
T_Blind_fast_RLM_TRUE	0.69	0.579	0.698	0.714	0.873	0.833
T_Blind_pearson_LM_FALSE	0.706	0.714	0.698	0.817	0.873	0.825
T_Blind_pearson_LM_TRUE	0.69	0.746	0.698	0.714	0.873	0.833
T_Blind_pearson_RLM_FALSE	0.81	0.81	0.802	0.81	0.833	0.794
T_Blind_pearson_RLM_TRUE	0.825	0.825	0.817	0.873	0.881	0.817
T_Blind_spearman_LM_FALSE	0.794	0.73	0.825	0.857	0.865	0.833
T_Blind_spearman_LM_TRUE	0.754	0.81	0.794	0.849	0.849	0.833
T_Blind_spearman_RLM_FALSE	0.802	0.635	0.714	0.833	0.817	0.817
T_Blind_spearman_RLM_TRUE	0.754	0.762	0.77	0.802	0.786	0.833
T_Driven_fast_LM_FALSE	0.746	0.794	0.794	0.865	0.873	0.817
T_Driven_fast_LM_TRUE	0.706	0.698	0.738	0.802	0.849	0.841
T_Driven_fast_RLM_FALSE	0.746	0.794	0.794	0.865	0.873	0.817
T_Driven_fast_RLM_TRUE	0.706	0.698	0.738	0.802	0.849	0.841
T_Driven_pearson_LM_FALSE	0.706	0.794	0.794	0.865	0.873	0.817
T_Driven_pearson_LM_TRUE	0.706	0.786	0.833	0.802	0.849	0.841
T_Driven_pearson_RLM_FALSE	0.802	0.817	0.802	0.817	0.77	0.817
T_Driven_pearson_RLM_TRUE	0.802	0.802	0.778	0.802	0.865	0.825
T_Driven_spearman_LM_FALSE	0.754	0.778	0.802	0.817	0.841	0.825
T_Driven_spearman_LM_TRUE	0.706	0.81	0.778	0.825	0.849	0.817
T_Driven_spearman_RLM_FALSE	0.706	0.786	0.746	0.825	0.802	0.802
T_Driven_spearman_RLM_TRUE	0.706	0.833	0.778	0.865	0.841	0.81

miny = 0.15
maxy = max(pbKNNaucM)

plot(thr,pbKNNaucM[1,],ylim=c(miny,maxy),
     main="KNN's ROCAUC",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="ROC AUC",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
  lines(thr,pbKNNaucM[ind,],col=ind,lwd=2,lty=ind)
}

legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)

fastRows <- str_detect(rownames(pbKNNaucM),"fast")
pearsonRows <- str_detect(rownames(pbKNNaucM),"pearson")
spearmanRows <- str_detect(rownames(pbKNNaucM),"spearman")
T_BlindRows <- str_detect(rownames(pbKNNaucM),"T_Blind")
corRankRows <- str_detect(rownames(pbKNNaucM),"TRUE")
maxCorRankRows <- str_detect(rownames(pbKNNaucM),"FALSE")
RLMfitMethod <- str_detect(rownames(pbKNNaucM),"RLM")


meanAuc <-  colMeans(pbKNNaucM[fastRows,])
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[pearsonRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[spearmanRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[!T_BlindRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[T_BlindRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[corRankRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[maxCorRankRows,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[RLMfitMethod,]))
meanAuc <-  rbind(meanAuc,colMeans(pbKNNaucM[!RLMfitMethod,]))
legnames <- c("fast","Pearson","Spearman","T_Driven","T_Blind","SumCor","MaxCor","RLM","LM")


pbKNNaccM <- as.data.frame(pbKNNaccM)
pbKNNaccM[,1:ncol(pbKNNaccM)] <- sapply(pbKNNaccM,as.numeric)

Average_Latent_SizeM <- as.data.frame(Average_Latent_SizeM)
Average_Latent_SizeM[,1:ncol(Average_Latent_SizeM)] <- sapply(Average_Latent_SizeM,as.numeric)
Average_Latent_SizeM[is.na(Average_Latent_SizeM)] <- 0

SigDeM <- as.data.frame(SigDeM)
SigDeM[,1:ncol(SigDeM)] <- sapply(SigDeM,as.numeric)

sparcityM <- as.data.frame(sparcityM)
sparcityM[,1:ncol(sparcityM)] <- sapply(sparcityM,as.numeric)

miny = 0.65
maxy = max(meanAuc)+0.025

plot(thr,meanAuc[1,],ylim=c(miny,maxy),
     main="Mean KNN's ROCAUC",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="ROC AUC",
     type="l",
     col=1,
     lwd=2,
     lty=1)
for (ind in 2:nrow(meanAuc))
{
  lines(thr,meanAuc[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 0.35
maxy = max(pbKNNaccM) + 0.1

plot(thr,pbKNNaccM[1,],ylim=c(miny,maxy),
     main="KNN's Accuracy",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Accuracy",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(pbKNNaucM))
{
  lines(thr,pbKNNaccM[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", rownames(pbKNNaucM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)



meanAcc <-  colMeans(pbKNNaccM[fastRows,])
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[pearsonRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[spearmanRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[!T_BlindRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[T_BlindRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[corRankRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[maxCorRankRows,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[RLMfitMethod,]))
meanAcc <-  rbind(meanAcc,colMeans(pbKNNaccM[!RLMfitMethod,]))

miny = min(meanAcc)-0.01
maxy = max(meanAcc)+0.025

plot(thr,meanAcc[1,],ylim=c(miny,maxy),
     main="Mean KNN's Accuracy",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Accuracy",
     type="l",
     col=1,
     lwd=2)
for (ind in 2:nrow(meanAcc))
{
  lines(thr,meanAcc[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = 1
maxy = 20*max(Average_Latent_SizeM)

plot(thr,Average_Latent_SizeM[1,],ylim=c(miny,maxy),
     main="Average Size of Latent-Variable",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Size",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(Average_Latent_SizeM))
{
  lines(thr,Average_Latent_SizeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(Average_Latent_SizeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanAccAvgSize <-  colMeans(Average_Latent_SizeM[fastRows,])
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[pearsonRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[spearmanRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!T_BlindRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[T_BlindRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[corRankRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[maxCorRankRows,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[RLMfitMethod,]))
meanAccAvgSize <-  rbind(meanAccAvgSize,colMeans(Average_Latent_SizeM[!RLMfitMethod,]))

miny =1
maxy = 5*max(meanAccAvgSize)

plot(thr,meanAccAvgSize[1,],ylim=c(miny,maxy),
     main="Mean Size of Average-Latent-Variable",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Size",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanAccAvgSize))
{
  lines(thr,meanAccAvgSize[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

miny = min(La_SignificantM)
maxy = 20*max(La_SignificantM)

plot(thr,La_SignificantM[1,],ylim=c(miny,maxy),
     main="Number of Discovered Features",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Number of Features",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(La_SignificantM))
{
  lines(thr,La_SignificantM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topleft", rownames(La_SignificantM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanDiscovered <-  colMeans(La_SignificantM[fastRows,])
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[pearsonRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[spearmanRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[!T_BlindRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[T_BlindRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[corRankRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[maxCorRankRows,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[RLMfitMethod,]))
meanDiscovered <-  rbind(meanDiscovered,colMeans(La_SignificantM[!RLMfitMethod,]))

miny = min(meanDiscovered)
maxy = max(meanDiscovered) + 10

plot(thr,meanDiscovered[1,],ylim=c(miny,maxy),
     main="Average Number of Discovered Features",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Number of Features",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanDiscovered))
{
  lines(thr,meanDiscovered[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

SigDeM[is.na(SigDeM)] <- 0
miny = 1
maxy = 20*max(SigDeM)

plot(thr,SigDeM[1,],ylim=c(miny,maxy),
     main="Number of Significant Latent Variables",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="How Many",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(SigDeM))
{
  lines(thr,SigDeM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(SigDeM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)



SigLatent <-  colMeans(SigDeM[fastRows,])
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[pearsonRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[spearmanRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[!T_BlindRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[T_BlindRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[corRankRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[maxCorRankRows,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[RLMfitMethod,]))
SigLatent <-  rbind(SigLatent,colMeans(SigDeM[!RLMfitMethod,]))

miny = 1
maxy = max(SigLatent) + 10


plot(thr,SigLatent[1,],ylim=c(miny,maxy),
     main="Average # of Significant Latent Variables",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="How Many",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(SigLatent))
{
  lines(thr,SigLatent[ind,],col=ind,lwd=2,lty=ind)
}
legend("bottomright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

sparcityM[is.na(sparcityM)] <- 0
miny = min(sparcityM)
maxy = max(sparcityM) + 0.75 

plot(thr,sparcityM[1,],ylim=c(miny,maxy),
     main="Matrix Sparcity",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Sparcity",
     type="l",
     col=1,
     lwd=2,
     log = "y")

for (ind in 2:nrow(sparcityM))
{
  lines(thr,sparcityM[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", rownames(sparcityM),lty=1:length(thenames), col = 1:length(thenames),cex=0.55,ncol=2)




meanSparcity <-  colMeans(sparcityM[fastRows,])
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[pearsonRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[spearmanRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[!T_BlindRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[T_BlindRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[corRankRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[maxCorRankRows,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[RLMfitMethod,]))
meanSparcity <-  rbind(meanSparcity,colMeans(sparcityM[!RLMfitMethod,]))

miny = min(meanSparcity)
maxy = max(meanSparcity)+0.25


plot(thr,meanSparcity[1,],ylim=c(miny,maxy),
     main="Mean Matrix Sparcity",
     xlab="Correlation-Matrix's Maximum Goal",
     ylab="Sparcity",
     type="l",
     col=1,
     lwd=2,
     log = "y")
for (ind in 2:nrow(meanSparcity))
{
  lines(thr,meanSparcity[ind,],col=ind,lwd=2,lty=ind)
}
legend("topright", legnames,lty=1:length(legnames), col = 1:length(legnames),cex=0.75)

1.2.2 Saving All

save.image("~/GitHub/LatentBiomarkers/RMD/Parkinsons_ALL_Options.RData")